Fall 2010: Unsupervised Feature Learning

Course Description: In recent years, there has been much interest in algorithms thatlearn feature hierarchies from unlabeled data. In particular, deeplearning methods such as deep belief networks, sparse coding-based methods, convolutional networks, and deep Boltzmann machines, have shown promise and have already been successfully applied to a variety of tasks in computer vision, audio processing, natural language processing, information retrieval, and robotics.

In this seminar course, we will focus on reviewing principles andrecent progress in unsupervised learning and deep learning algorithms, with a goal of developing useful features for machine learning applications. Topics include sparse coding, autoencoders, restricted Boltzmann machines, and deep belief networks. The course will require an open-ended research project.